A method, apparatus, computer device, and storage medium for automatic design of analog circuits based on tree structure. The method includes: setting the maximum height and growth direction of the tree structure; randomly calling the node from the function node library as the parent node; randomly calling the node from the function node library and the port node library as the child according to the growth direction node; if the child node is a terminal node, generating a tree structure; checking the tree structure, if the tree structure satisfies the preset conditions, obtaining the circuit topology and device parameter that conform to the circuit rules; evolving the circuit topology and device parameter to generate an analog circuit. The embodiments achieve the effect of making the tree structure of the designed analog circuit more reasonable.

A method, apparatus, computer device, and storage medium for automatic design of analog circuits based on tree structure. The method includes: setting the maximum height and growth direction of the tree structure; randomly calling the node from the function node library as the parent node; randomly calling the node from the function node library and the port node library as the child according to the growth direction node; if the child node is a terminal node, generating a tree structure; checking the tree structure, if the tree structure satisfies the preset conditions, obtaining the circuit topology and device parameter that conform to the circuit rules; evolving the circuit topology and device parameter to generate an analog circuit. The embodiments achieve the effect of making the tree structure of the designed analog circuit more reasonable.

The optimization of circuit parameters of variational quantum algorithms is a challenge for the practical deployment of near-term quantum computing algorithms. Embodiments relate to a hybrid quantum-classical optimization methods. In a first stage, analytical tomography fittings are performed for a local cluster of circuit parameters via sampling of the observable objective function at quadrature points in the circuit parameters. Optimization may be used to determine the optimal circuit parameters within the cluster, with the other circuit parameters frozen. In a second stage, different clusters of circuit parameters are then optimized in “Jacobi sweeps,” leading to a monotonically convergent fixed-point procedure. In a third stage, the iterative history of the fixed-point Jacobi procedure may be used to accelerate the convergence by applying Anderson acceleration/Pulay's direct inversion of the iterative subspace (DIIS).

The optimization of circuit parameters of variational quantum algorithms is a challenge for the practical deployment of near-term quantum computing algorithms. Embodiments relate to a hybrid quantum-classical optimization methods. In a first stage, analytical tomography fittings are performed for a local cluster of circuit parameters via sampling of the observable objective function at quadrature points in the circuit parameters. Optimization may be used to determine the optimal circuit parameters within the cluster, with the other circuit parameters frozen. In a second stage, different clusters of circuit parameters are then optimized in “Jacobi sweeps,” leading to a monotonically convergent fixed-point procedure. In a third stage, the iterative history of the fixed-point Jacobi procedure may be used to accelerate the convergence by applying Anderson acceleration/Pulay's direct inversion of the iterative subspace (DIIS).

Systems and methods are described herein for attribute-point-based timing formal verification of application specific integrated circuit (ASIC) and system on chip (SoC) designs. A target circuit design having a first set of netlists and timing constraints is received. A plurality of key clock-pin-net-load-setting attributes are extracted from the first ported netlists and timing constraints. The clock-pin-net-load-setting attribute mismatch in the result report is checked between the target circuit design and a golden circuit design by comparing the plurality of target attributes with a plurality of golden attributes of the golden circuit design after the target design database is loaded for static timing analysis (STA). The attribute mismatch is provided for further design or timing constraint modifications and/or updates using this approach, particularly timing formal verification, at the target technology in order to enable efficient design timing sign-off based on ported netlists and synthesis design constraints (SDC).

A method includes generating a plurality of vector sequences based on input signals of an electric circuit design and encoding the plurality of vector sequences. The method also includes clustering the plurality of encoded vector sequences into a plurality of clusters and selecting a set of encoded vector sequences from the plurality of clusters. The method further includes selecting a first set of vector sequences corresponding to the selected set of encoded vector sequences, selecting a second set of vector sequences from the plurality of vector sequences not in the first set of encoded vector sequences, and training, by a processing device, a machine learning model to predict power consumption using the first and second sets of vector sequences.

Systems and methods are described herein for attribute-point-based timing formal verification of application specific integrated circuit (ASIC) and system on chip (SoC) designs. A target circuit design having a first set of netlists and timing constraints is received. A plurality of key clock-pin-net-load-setting attributes are extracted from the first ported netlists and timing constraints. The clock-pin-net-load-setting attribute mismatch in the result report is checked between the target circuit design and a golden circuit design by comparing the plurality of target attributes with a plurality of golden attributes of the golden circuit design after the target design database is loaded for static timing analysis (STA). The attribute mismatch is provided for further design or timing constraint modifications and/or updates using this approach, particularly timing formal verification, at the target technology in order to enable efficient design timing sign-off based on ported netlists and synthesis design constraints (SDC).

Generating an integrated circuit (IC) includes receiving Design For Testability (DFT) Compressor Decompressor (CODEC) circuitry of an integrated circuit (IC) design, and partitioning the DFT CODEC circuitry into two or more sub-blocks based on a number of scan chains within the IC design. Further, scan chains are assigned to each of the two or more sub-blocks based on locations of end points within the scan chains. A layout of the IC design is generated by placing the DFT CODEC circuitry within the IC design based the locations of end points within the scan chains and the assigned scan chains to each of the two or more sub-blocks.

Generating an integrated circuit (IC) includes receiving Design For Testability (DFT) Compressor Decompressor (CODEC) circuitry of an integrated circuit (IC) design, and partitioning the DFT CODEC circuitry into two or more sub-blocks based on a number of scan chains within the IC design. Further, scan chains are assigned to each of the two or more sub-blocks based on locations of end points within the scan chains. A layout of the IC design is generated by placing the DFT CODEC circuitry within the IC design based the locations of end points within the scan chains and the assigned scan chains to each of the two or more sub-blocks.

A simulation apparatus has: a first processing part configured to obtain a value of a parameter in a first set relating to the forming of the pattern; a second processing part configured to obtain a value of a parameter in a second set that is at least partially same as the parameter in the first set and relating to the forming of the pattern; and an integration processing part configured to evaluate, based on the value of the parameter in the first set and the value of the parameter in the second set, a state of the pattern formed on the substrate and a forming condition when the pattern is formed, and to determine based on the result of the evaluation whether or not to make at least one of the first processing part and the second processing part recalculate the value of the parameter in the corresponding set.